In-Season Prediction of Corn Grain Yield through PlanetScope and Sentinel-2 Images
Crop growth and yield monitoring are essential for food security and agricultural economic return prediction. Remote sensing is an efficient technique for measuring growing season crop canopies and providing information on the spatial variability of crop yields. In this study, ten vegetation indices...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-12-01
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| Series: | Agronomy |
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| Online Access: | https://www.mdpi.com/2073-4395/12/12/3176 |
| _version_ | 1827642224192520192 |
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| author | Fenling Li Yuxin Miao Xiaokai Chen Zhitong Sun Kirk Stueve Fei Yuan |
| author_facet | Fenling Li Yuxin Miao Xiaokai Chen Zhitong Sun Kirk Stueve Fei Yuan |
| author_sort | Fenling Li |
| collection | DOAJ |
| description | Crop growth and yield monitoring are essential for food security and agricultural economic return prediction. Remote sensing is an efficient technique for measuring growing season crop canopies and providing information on the spatial variability of crop yields. In this study, ten vegetation indices (VIs) derived from time series PlanetScope and Sentinel-2 images were used to investigate the potential to estimate corn grain yield with different regression methods. A field-scale spatial crop yield prediction model was developed and used to produce yield maps depicting spatial variability in the field. Results from this study clearly showed that high-resolution PlanetScope satellite data could be used to detect the corn yield variability at field level, which could explain 15% more variability than Sentinel-2A data at the same spatial resolution of 10 m. Comparison of the model performance and variable importance measure between models illustrated satisfactory results for assessing corn productivity with VIs. The green chlorophyll vegetation index (GCVI) values consistently produced the highest correlations with corn yield, accounting for 72% of the observed spatial variation in corn yield. More reliable quantitative yield estimation could be made using a multi-linear stepwise regression (MSR) method with multiple VIs. Good agreement between observed and predicted yield was achieved with the coefficient of determination value being 0.81 at 86 days after seeding. The results would help farmers and decision-makers generate predicted yield maps, identify crop yield variability, and make further crop management practices timely. |
| first_indexed | 2024-03-09T17:25:30Z |
| format | Article |
| id | doaj.art-f0fe696cff5a4411b44469b2587c9a6b |
| institution | Directory Open Access Journal |
| issn | 2073-4395 |
| language | English |
| last_indexed | 2024-03-09T17:25:30Z |
| publishDate | 2022-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agronomy |
| spelling | doaj.art-f0fe696cff5a4411b44469b2587c9a6b2023-11-24T12:47:45ZengMDPI AGAgronomy2073-43952022-12-011212317610.3390/agronomy12123176In-Season Prediction of Corn Grain Yield through PlanetScope and Sentinel-2 ImagesFenling Li0Yuxin Miao1Xiaokai Chen2Zhitong Sun3Kirk Stueve4Fei Yuan5College of Natural Resources and Environment, Northwest A&F University, Yangling, Xianyang 712100, ChinaPrecision Agriculture Center, Department of Soil, Water, and Climate, University of Minnesota, St. Paul, MN 55108, USACollege of Natural Resources and Environment, Northwest A&F University, Yangling, Xianyang 712100, ChinaCollege of Natural Resources and Environment, Northwest A&F University, Yangling, Xianyang 712100, ChinaCeres Imaging, Oakland, CA 94612, USADepartment of Geography, Minnesota State University, Mankato, MN 56001, USACrop growth and yield monitoring are essential for food security and agricultural economic return prediction. Remote sensing is an efficient technique for measuring growing season crop canopies and providing information on the spatial variability of crop yields. In this study, ten vegetation indices (VIs) derived from time series PlanetScope and Sentinel-2 images were used to investigate the potential to estimate corn grain yield with different regression methods. A field-scale spatial crop yield prediction model was developed and used to produce yield maps depicting spatial variability in the field. Results from this study clearly showed that high-resolution PlanetScope satellite data could be used to detect the corn yield variability at field level, which could explain 15% more variability than Sentinel-2A data at the same spatial resolution of 10 m. Comparison of the model performance and variable importance measure between models illustrated satisfactory results for assessing corn productivity with VIs. The green chlorophyll vegetation index (GCVI) values consistently produced the highest correlations with corn yield, accounting for 72% of the observed spatial variation in corn yield. More reliable quantitative yield estimation could be made using a multi-linear stepwise regression (MSR) method with multiple VIs. Good agreement between observed and predicted yield was achieved with the coefficient of determination value being 0.81 at 86 days after seeding. The results would help farmers and decision-makers generate predicted yield maps, identify crop yield variability, and make further crop management practices timely.https://www.mdpi.com/2073-4395/12/12/3176corn yieldPlanetScopeSentinel-2vegetation indexmulti-linear stepwise regressionrandom forest regression |
| spellingShingle | Fenling Li Yuxin Miao Xiaokai Chen Zhitong Sun Kirk Stueve Fei Yuan In-Season Prediction of Corn Grain Yield through PlanetScope and Sentinel-2 Images Agronomy corn yield PlanetScope Sentinel-2 vegetation index multi-linear stepwise regression random forest regression |
| title | In-Season Prediction of Corn Grain Yield through PlanetScope and Sentinel-2 Images |
| title_full | In-Season Prediction of Corn Grain Yield through PlanetScope and Sentinel-2 Images |
| title_fullStr | In-Season Prediction of Corn Grain Yield through PlanetScope and Sentinel-2 Images |
| title_full_unstemmed | In-Season Prediction of Corn Grain Yield through PlanetScope and Sentinel-2 Images |
| title_short | In-Season Prediction of Corn Grain Yield through PlanetScope and Sentinel-2 Images |
| title_sort | in season prediction of corn grain yield through planetscope and sentinel 2 images |
| topic | corn yield PlanetScope Sentinel-2 vegetation index multi-linear stepwise regression random forest regression |
| url | https://www.mdpi.com/2073-4395/12/12/3176 |
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